Machine Learning for Engineers I 2021/2022 /CoursesID:2361

Detailed information

Keywords: machine learning

Most recent entry on 2021-06-21 

Organisational Unit

Friedrich-Alexander-Universität Erlangen-Nürnberg

Recording type

Vorlesungsreihe

Language

English

This course offers an overview of some of the most widely used machine learning (ML) methods that are required for solving data science problems. We present the necessary fundamental for each topic and provide programming exercises. The course includes:

  • The common practices for data pre-processing.
  • Teaching different tasks regarding regression, classification, and dimensionality reduction using methods including but not limited to linear regression and classification, Support vector machines and Deep neural networks.
  • Introduction to Python programming for data science.
  • Applying machine learning models on real world engineering applications

Course chapters

Episode
Title
Lecturer
Updated
Via
Duration
Media
1
Lecture 1: Organizational Information
Prof. Dr. Björn Eskofier
2021-04-14
Studon
00:05:56
2
Lecture 1: Motivation
Prof. Dr. Björn Eskofier
2021-04-14
Studon
00:20:17
3
Lecture 1: Machine Learning Types
Prof. Dr. Björn Eskofier
2021-04-14
Studon
00:28:56
4
Lecture 1: Machine Learning Pipeline
Prof. Dr. Björn Eskofier
2021-04-14
Studon
00:14:12
5
Lecture 1: Summary
Prof. Dr. Björn Eskofier
2021-04-15
Studon
00:02:30
Episode
Title
Lecturer
Updated
Via
Duration
Media
6
Lecture 2: Motivation
Prof. Dr. Björn Eskofier
2021-04-27
Studon
00:10:26
7
Lecture 2: Linear Regression - Overall Picture
Prof. Dr. Björn Eskofier
2021-04-27
Studon
00:04:44
8
Lecture 2: Linear Regression - Model
Prof. Dr. Björn Eskofier
2021-04-27
Studon
00:13:16
9
Lecture 2: Linear Regression - Optimization
Prof. Dr. Björn Eskofier
2021-04-27
Studon
00:26:43
25
Lecture 2: Linear Regression - Basis Functions
Prof. Dr. Björn Eskofier
2021-06-21
Studon
00:10:59
26
Lecture 2: Logistic Regression - Motivation
Prof. Dr. Björn Eskofier
2021-06-21
Studon
00:06:14
27
Lecture 2: Logistic Regression - Framework
Prof. Dr. Björn Eskofier
2021-06-21
Studon
00:14:54
28
Lecture 2: Overfitting and Underfitting
Prof. Dr. Björn Eskofier
2021-06-21
Studon
00:12:08
Episode
Title
Lecturer
Updated
Via
Duration
Media
10
Lecture 3: Problem Statement
Prof. Dr. Björn Eskofier
2021-05-11
Studon
00:11:43
11
Lecture 3: Optimization
Prof. Dr. Björn Eskofier
2021-05-11
Studon
00:12:50
12
Lecture 3: Kernel Trick
Prof. Dr. Björn Eskofier
2021-05-11
Studon
00:18:10
13
Lecture 3: Hard and Soft Margin
Prof. Dr. Björn Eskofier
2021-05-11
Studon
00:06:43
14
Lecture 3: Regression
Prof. Dr. Björn Eskofier
2021-05-11
Studon
00:07:35
15
Lecture 3: Summary
Prof. Dr. Björn Eskofier
2021-05-11
Studon
00:03:43
Episode
Title
Lecturer
Updated
Via
Duration
Media
16
Lecture 4: Intuition
Prof. Dr. Björn Eskofier
2021-05-25
Studon
00:07:14
17
Lecture 4: Mathematics
Prof. Dr. Björn Eskofier
2021-05-25
Studon
00:12:26
18
Lecture 4: Applications
Prof. Dr. Björn Eskofier
2021-05-25
Studon
00:10:52
19
Lecture 4: Summary
Prof. Dr. Björn Eskofier
2021-05-25
Studon
00:03:00
Episode
Title
Lecturer
Updated
Via
Duration
Media
20
Lecture 5: Perceptron
Prof. Dr. Björn Eskofier
2021-06-08
Studon
00:09:28
21
Lecture 5: Multilayer Perceptron
Prof. Dr. Björn Eskofier
2021-06-08
Studon
00:07:41
22
Lecture 5: Loss Function
Prof. Dr. Björn Eskofier
2021-06-08
Studon
00:10:38
23
Lecture 5: Gradient Descent
Prof. Dr. Björn Eskofier
2021-06-08
Studon
00:10:03
24
Lecture 5: Learning Process
Prof. Dr. Björn Eskofier
2021-06-08
Studon
00:07:05
29
Lecture 5: Convolution
Prof. Dr. Björn Eskofier
2021-06-21
Studon
00:10:31
30
Lecture 5: Pooling
Prof. Dr. Björn Eskofier
2021-06-21
Studon
00:08:03
31
Lecture 5: Applications
Prof. Dr. Björn Eskofier
2021-06-21
Studon
00:06:05

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